Breast cancer is a significant global health concern, particularly for women.
Early detection and appropriate treatment are crucial in mitigating its impact,
with histopathology examinations playing a vital role in swift diagnosis.
However, these examinations often require a substantial workforce and
experienced medical experts for proper recognition and cancer grading.
Automated image retrieval systems have the potential to assist pathologists in
identifying cancerous tissues, thereby accelerating the diagnostic process.
Nevertheless, due to considerable variability among the tissue and cell
patterns in histological images, proposing an accurate image retrieval model is
very challenging.
This work introduces a novel attention-based adversarially regularized
variational graph autoencoder model for breast histological image retrieval.
Additionally, we incorporated cluster-guided contrastive learning as the graph
feature extractor to boost the retrieval performance. We evaluated the proposed
model's performance on two publicly available datasets of breast cancer
histological images and achieved superior or very competitive retrieval
performance, with average mAP scores of 96.5% for the BreakHis dataset and
94.7% for the BACH dataset, and mVP scores of 91.9% and 91.3%, respectively.
Our proposed retrieval model has the potential to be used in clinical
settings to enhance diagnostic performance and ultimately benefit patients.

提出了一种用于乳腺组织图像检索的新型基于注意力的对抗正则化变分图自编码器模型，结合聚类引导的对比学习作为图特征提取器，评估其在乳腺癌组织图像数据集上的性能并取得了卓越的结果，具有在临床环境中提高诊断性能的潜力，从而最终造福于患者。

基于注意力对抗正则化变分图自编码器的乳腺组织病理图像检索与对比学习特征提取

Breast Histopathology Image Retrieval by Attention-based Adversarially  Regularized Variational Graph Autoencoder with Contrastive Learning-Based  Feature Extraction